Pub Date : 2024-06-01DOI: 10.4208/cicp.oa-2023-0208
Adam Larios, Mark Petersen null, Collin Victor
{"title":"Application of Continuous Data Assimilation in High-Resolution Ocean Modeling","authors":"Adam Larios, Mark Petersen null, Collin Victor","doi":"10.4208/cicp.oa-2023-0208","DOIUrl":"https://doi.org/10.4208/cicp.oa-2023-0208","url":null,"abstract":"","PeriodicalId":50661,"journal":{"name":"Communications in Computational Physics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141413699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.4208/cicp.oa-2023-0078
Lizuo Liu,Kamaljyoti Nath, Wei Cai
In this paper, we propose a DeepONet structure with causality to represent causal linear operators between Banach spaces of time-dependent signals. The theorem of universal approximations to nonlinear operators proposed in [5] is extended to operators with causalities, and the proposed Causality-DeepONet implements the physical causality in its framework. The proposed Causality-DeepONet considers causality (the state of the system at the current time is not affected by that of the future, but only by its current state and past history) and uses a convolution-type weight in its design. To demonstrate its effectiveness in handling the causal response of a physical system, the Causality-DeepONet is applied to learn the operator representing the response of a building due to earthquake ground accelerations. Extensive numerical tests and comparisons with some existing variants of DeepONet are carried out, and the Causality-DeepONet clearly shows its unique capability to learn the retarded dynamic responses of the seismic response operator with good accuracy.
{"title":"A Causality-DeepONet for Causal Responses of Linear Dynamical Systems","authors":"Lizuo Liu,Kamaljyoti Nath, Wei Cai","doi":"10.4208/cicp.oa-2023-0078","DOIUrl":"https://doi.org/10.4208/cicp.oa-2023-0078","url":null,"abstract":"In this paper, we propose a DeepONet structure with causality to represent\u0000causal linear operators between Banach spaces of time-dependent signals. The theorem of universal approximations to nonlinear operators proposed in [5] is extended\u0000to operators with causalities, and the proposed Causality-DeepONet implements the\u0000physical causality in its framework. The proposed Causality-DeepONet considers\u0000causality (the state of the system at the current time is not affected by that of the future, but only by its current state and past history) and uses a convolution-type weight\u0000in its design. To demonstrate its effectiveness in handling the causal response of a\u0000physical system, the Causality-DeepONet is applied to learn the operator representing\u0000the response of a building due to earthquake ground accelerations. Extensive numerical tests and comparisons with some existing variants of DeepONet are carried out,\u0000and the Causality-DeepONet clearly shows its unique capability to learn the retarded\u0000dynamic responses of the seismic response operator with good accuracy.","PeriodicalId":50661,"journal":{"name":"Communications in Computational Physics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel 3-D full electromagnetic particle-in-cell (PIC) code called JefiPIC, which uses Jefimenko’s equations as the electromagnetic (EM) field solver through a full-space integration method. Leveraging the power of state-of-the-art graphic processing units (GPUs), we have made the challenging integral task of PIC simulations achievable. Our proposed code offers several advantages by utilizing the integral method. Firstly, it offers a natural solution for modeling non-neutral plasmas without the need for pre-processing such as solving Poisson’s equation. Secondly, it eliminates the requirement for designing elaborate boundary layers to absorb fields and particles. Thirdly, it maintains the stability of the plasma simulation regardless of the time step chosen. Lastly, it does not require strict charge-conservation particle-to-grid apportionment techniques or electric field divergence amendment algorithms, which are commonly used in finite-difference time-domain (FDTD)-based PIC simulations. To validate the accuracy and advantages of our code, we compared the evolutions of particles and fields in different plasma systems simulated by three other codes. Our results demonstrate that the combination of Jefimenko’s equations and the PIC method can produce accurate particle distributions and EM fields in open-boundary plasma systems. Additionally, our code is able to accomplish these computations within an acceptable execution time. This study highlights the effectiveness and efficiency of JefiPIC, showing its potential for advancing plasma simulations.
{"title":"JefiPIC: A 3-D Full Electromagnetic Particle-in-Cell Simulator Based on Jefimenko’s Equations on GPU","authors":"Jian-Nan Chen,Jun-Jie Zhang,Hai-Liang Qiao,Xue-Ming Li, Yong-Tao Zhao","doi":"10.4208/cicp.oa-2023-0156","DOIUrl":"https://doi.org/10.4208/cicp.oa-2023-0156","url":null,"abstract":"This paper presents a novel 3-D full electromagnetic particle-in-cell (PIC)\u0000code called JefiPIC, which uses Jefimenko’s equations as the electromagnetic (EM) field\u0000solver through a full-space integration method. Leveraging the power of state-of-the-art graphic processing units (GPUs), we have made the challenging integral task of\u0000PIC simulations achievable. Our proposed code offers several advantages by utilizing the integral method. Firstly, it offers a natural solution for modeling non-neutral\u0000plasmas without the need for pre-processing such as solving Poisson’s equation. Secondly, it eliminates the requirement for designing elaborate boundary layers to absorb\u0000fields and particles. Thirdly, it maintains the stability of the plasma simulation regardless of the time step chosen. Lastly, it does not require strict charge-conservation\u0000particle-to-grid apportionment techniques or electric field divergence amendment algorithms, which are commonly used in finite-difference time-domain (FDTD)-based\u0000PIC simulations. To validate the accuracy and advantages of our code, we compared\u0000the evolutions of particles and fields in different plasma systems simulated by three\u0000other codes. Our results demonstrate that the combination of Jefimenko’s equations\u0000and the PIC method can produce accurate particle distributions and EM fields in open-boundary plasma systems. Additionally, our code is able to accomplish these computations within an acceptable execution time. This study highlights the effectiveness\u0000and efficiency of JefiPIC, showing its potential for advancing plasma simulations.","PeriodicalId":50661,"journal":{"name":"Communications in Computational Physics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141515596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.4208/cicp.oa-2023-0201
Jiajun Zhan, Lei Yang, Rui Du null, Zixuan Cui
{"title":"Towards Preserving Geometric Properties of Landau-Lifshitz-Gilbert Equation Using Multistep Methods","authors":"Jiajun Zhan, Lei Yang, Rui Du null, Zixuan Cui","doi":"10.4208/cicp.oa-2023-0201","DOIUrl":"https://doi.org/10.4208/cicp.oa-2023-0201","url":null,"abstract":"","PeriodicalId":50661,"journal":{"name":"Communications in Computational Physics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141408652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.4208/cicp.oa-2023-0252
Han Li, Jialiang Lu, Hongjun Ji, Lizhan Hong null, Helin Gong
{"title":"A Noise and Vibration Tolerant Resnet for Field Reconstruction with Sparse Sensors","authors":"Han Li, Jialiang Lu, Hongjun Ji, Lizhan Hong null, Helin Gong","doi":"10.4208/cicp.oa-2023-0252","DOIUrl":"https://doi.org/10.4208/cicp.oa-2023-0252","url":null,"abstract":"","PeriodicalId":50661,"journal":{"name":"Communications in Computational Physics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141403985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.4208/cicp.oa-2023-0315
Zhongqin Xue, Guanghui Wen, Zhimin Zhang null, Xuan Zhao
{"title":"Efficient High-Order Backward Difference Formulae for Cahn-Hilliard Equation with the Gradient Flow in $H^{−α}$","authors":"Zhongqin Xue, Guanghui Wen, Zhimin Zhang null, Xuan Zhao","doi":"10.4208/cicp.oa-2023-0315","DOIUrl":"https://doi.org/10.4208/cicp.oa-2023-0315","url":null,"abstract":"","PeriodicalId":50661,"journal":{"name":"Communications in Computational Physics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141414434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.4208/cicp.oa-2023-0274
Guanlan Huang, Sebastiano Boscarino null, Tao Xiong
{"title":"High Order Asymptotic Preserving and Well-Balanced Schemes for the Shallow Water Equations with Source Terms","authors":"Guanlan Huang, Sebastiano Boscarino null, Tao Xiong","doi":"10.4208/cicp.oa-2023-0274","DOIUrl":"https://doi.org/10.4208/cicp.oa-2023-0274","url":null,"abstract":"","PeriodicalId":50661,"journal":{"name":"Communications in Computational Physics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141416010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.4208/cicp.oa-2023-0284
Yubin Lu, Chi-An Chen, Xiaofan Li null, Chun Liu
{"title":"Finite Difference Approximation with ADI Scheme for Two-Dimensional Keller-Segel Equations","authors":"Yubin Lu, Chi-An Chen, Xiaofan Li null, Chun Liu","doi":"10.4208/cicp.oa-2023-0284","DOIUrl":"https://doi.org/10.4208/cicp.oa-2023-0284","url":null,"abstract":"","PeriodicalId":50661,"journal":{"name":"Communications in Computational Physics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141402592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations (GRTEs). The system is challenging to be simulated with both the traditional numerical schemes and the vanilla physics-informed neural networks (PINNs) due to the multiscale characteristics. Under the framework of PINNs, we employ a micro-macro decomposition technique to construct a new asymptotic-preserving (AP) loss function, which includes the residual of the governing equations in the micro-macro coupled form, the initial and boundary conditions with additional diffusion limit information, the conservation laws, and a few labeled data. A convergence analysis is performed for the proposed method, and a number of numerical examples are presented to illustrate the efficiency of MD-APNNs, and particularly, the importance of the AP property in the neural networks for the diffusion dominating problems. The numerical results indicate that MD-APNNs lead to a better performance than APNNs or pure Data-driven networks in the simulation of the nonlinear non-stationary GRTEs.
{"title":"A Model-Data Asymptotic-Preserving Neural Network Method Based on Micro-Macro Decomposition for Gray Radiative Transfer Equations","authors":"Hongyan Li,Song Jiang,Wenjun Sun,Liwei Xu, Guanyu Zhou","doi":"10.4208/cicp.oa-2022-0315","DOIUrl":"https://doi.org/10.4208/cicp.oa-2022-0315","url":null,"abstract":"We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations (GRTEs). The\u0000system is challenging to be simulated with both the traditional numerical schemes\u0000and the vanilla physics-informed neural networks (PINNs) due to the multiscale characteristics. Under the framework of PINNs, we employ a micro-macro decomposition technique to construct a new asymptotic-preserving (AP) loss function, which includes the residual of the governing equations in the micro-macro coupled form, the\u0000initial and boundary conditions with additional diffusion limit information, the conservation laws, and a few labeled data. A convergence analysis is performed for the\u0000proposed method, and a number of numerical examples are presented to illustrate the\u0000efficiency of MD-APNNs, and particularly, the importance of the AP property in the\u0000neural networks for the diffusion dominating problems. The numerical results indicate that MD-APNNs lead to a better performance than APNNs or pure Data-driven\u0000networks in the simulation of the nonlinear non-stationary GRTEs.","PeriodicalId":50661,"journal":{"name":"Communications in Computational Physics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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